4.3 out of 5 stars
(7637 Ratings)
Wrike’s powerful work management platform enables distributed teams to collaborate in real-time on complex projects. Our versatile, cloud-based software is trusted by top tech companies across the globe, including Siemens and Fitbit.
Wrike’s award-winning features include cross-tagging, custom item types, dynamic request forms, and automated workflows. With our 400+ app integrations, you can streamline tasks and keep all your favorite tools in one place. Experience the power of voice commands and smart replies with our Work Intelligence™ software. We also offer pre-built templates designed for specific teams, helping you kick-start your sprint planning, manage Agile projects, assess risks, and adapt to unforeseen changes with ease.
Worried about keeping your data secure in the cloud? No problem! Our enterprise-grade security boasts 99.9% uptime, as well as continuous data backup, user authentication, role-based access control, and data encryption.
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5.0 out of 5 stars
(7880 Ratings)
Fathom is an AI notetaking platform that records, transcribes, summarizes, and organizes meetings so users can stay focused on the conversation. The platform supports bot-based and bot-free capture, giving individuals and teams flexible ways to capture meeting notes. Fathom provides accurate transcripts, instant summaries, action items, follow-up details, and searchable meeting history. Users can ask questions across meetings, monitor key topics, and turn conversations into next steps for sales, customer success, marketing, and internal teams. Fathom integrates with tools such as Google Meet, Zoom, Microsoft Teams, Gmail, Slack, Salesforce, HubSpot, Notion, Asana, ChatGPT, Claude, Zapier, and public API or MCP workflows. Built for teams and individuals, Fathom helps reduce meeting admin, improve follow-through, and keep important decisions visible.
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Text2Mesh
Text2Mesh produces color and geometric details over a variety of source meshes, driven by a target text prompt. Our stylization results coherently blend unique and ostensibly unrelated combinations of text, capturing both global semantics and part-aware attributes. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a learned neural network, which we term neural style field network. In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP. Text2Mesh requires neither a pre-trained generative model nor a specialized 3D mesh dataset. It can handle low-quality meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not require UV parameterization.
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